Development of high accuracy classifier for the speaker recognition system

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dc.authorid0000-0002-4782-3405en_US
dc.authorid0000-0002-4249-6951 Countries Turkeyen_US
dc.authorid0000-0002-1895-0333en_US
dc.contributor.authorAl-Hassani, Raghad Tariq
dc.contributor.authorAtilla, Doğu Çağdaş
dc.contributor.authorAydın, Çağatay
dc.date.accessioned2022-01-16T10:13:54Z
dc.date.available2022-01-16T10:13:54Z
dc.date.issued2021en_US
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesien_US
dc.description.abstractSpeech signal is enriched with plenty of features used for biometrical recognition and other applications like gender and emotional recognition. Channel conditions manifested by background noise and reverberation are the main challenges causing feature shifts in the test and training data. In this paper, a hybrid speaker identification model for consistent speech features and high recognition accuracy is made. Features using Mel frequency spectrum coefficients (MFCC) have been improved by incorporating a pitch frequency coefficient from speech time domain analysis. In order to enhance noise immunity, we proposed a single hidden layer feed-forward neural network (FFNN) tuned by an optimized particle swarm optimization (OPSO) algorithm. The proposed model is tested using 10-fold cross-validation over different levels of Adaptive White Gaussian Noise (AWGN) (0-50 dB). A recognition accuracy of 97.83% was obtained from the proposed model in clean voice environments. However, a noisy channel is realized with lesser impact on the proposed model as compared with other baseline classifiers such as plain-FFNN, random forest (RF), -nearest neighbour (KNN), and support vector machine (SVM).en_US
dc.identifier.citationAl-Hassani, R. T., Atilla, D. C., & Aydin, Ç. (2021). Development of High Accuracy Classifier for the Speaker Recognition System. Applied Bionics and Biomechanics, 2021.en_US
dc.identifier.pmid34104204
dc.identifier.urihttps://hdl.handle.net/20.500.12939/2172
dc.identifier.wosWOS:000658893000001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorAl-Hassani, Raghad Tariq
dc.institutionauthorAtilla, Doğu Çağdaş
dc.institutionauthorAydın, Çağatay
dc.language.isoen
dc.publisherHindawien_US
dc.relation.ispartofApplied Bionics and Biomechanics
dc.relation.isversionof10.1155/2021/5559616en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectFFNNen_US
dc.subjectOPSOen_US
dc.subjectMFCCen_US
dc.subjectKNNen_US
dc.titleDevelopment of high accuracy classifier for the speaker recognition system
dc.typeArticle

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